Electronic health records typically contain codified diagnoses (frequently using standard coding schemes such as ICD9 or SnoMed) such records may also comprise patient clinical problem lists that are required for billing purposes, as well as for maintaining a patient clinical problem list, for subsequent data mining, and for clinical decision support. In a patient data sharing healthcare ecosystem, however, there is co-mingling of diagnoses from multiple providers (who may not know that they may all be treating the same patient), and diagnoses from insurers, which typically include diagnoses amalgamated from suppliers, laboratories, hospitals, nursing homes, imaging facilities, pharmacies, and other entities whose products and services require a diagnosis for payment.
There is therefore a difficulty in determining in a single patient which diagnoses are real and/or currently active when data is shared. These include diagnoses: (1) that are presumptive and created for reimbursement of legitimate services (presumptive diagnosis), (2) diagnoses that are created for services not actually rendered (fraud), (3) diagnoses that do not represent the most severe manifestation of a disease (known as “undercoding”) because of lack of caregiver time to encode the most specific condition (e.g., plain “diabetes” vs. “Uncontrolled Type II diabetes with renal manifestations”), (4) diagnoses captured in health records by inadequately trained personnel across the entire spectrum of caregivers (from a generalist physician, to a specialist, to a nurse, to a pharmacist, to a technical assisting with documentation, to self-reported diagnoses by the patient), and finally (5) diagnoses that were once true (e.g., knee sprain, influenza, or routine urinary tract infection from 1 year ago) but would be expected to have time-expired due to the natural history of the illness, and no longer present in a patient's active diagnoses today.
In the case of presumptive diagnosis, by way of example, an imaging facility or laboratory typically records what is known as a ‘presumptive’ or ‘rule-out’ diagnosis when it submits a bill for payment. For example, a chest x-ray is commonly associated with a diagnosis of ‘pneumonia’ when in fact that it is a presumptive diagnosis to include with the image. In many, if not most, instances when a chest x-ray is performed, pneumonia is not found, yet the presumptive diagnosis persists in the patient's medical history.
In electronic health record (EHR) sharing environments, all of these diagnoses can become humanly impossible to sift through in the few minutes a doctor has to treat a new patient, for example.
Therefore, it is desirable to have a system and method in which the veracity of the diagnoses (stored in a patient medical record) can be determined across one or more amalgamated sources of diagnosis data (an example of an amalgamated source of diagnosis date may be a multi-provider health care record or health care records from multiple health care providers) at the current point of care and it is to this end that the disclosure is directed.
Various care data concerning a patient is weighted and processed to provide a likelihood indicator of whether the diagnosis is accurate. The invention can also be used to provide alternative diagnosis for a care provider to consider, ongoing feedback may be used to enhance the reliability of the invention output.